vm-tools | run VM clusters on your Linux workstation | Infrastructure Automation library

The SDK and community support includes standard implementations of recognizers

RegExRecognizer – useful for simple recognition tasks such as unit tests.

LuisRecognizer – use LUIS.ai model for intent/entity recognition

QnAMakerRecognizer – use QnAMaker KB for recognizing FAQs

CrossTrainedRecognizer – combines multiple-recognizers that are cross-trained together (such as Luis/Qna)

Community recognizers – BERT, spaCy.io, DialogFlow, etc.

The SDK defines a common declarative file for language recognition called a .LU file. LU files are simple markdown files which are easy to edit and capture the basic NLU concepts of intents, entities, labeled data, dictionaries, etc. The SDK provides a plugin to BF CLI which parses LU files and creates a LUIS model from it. Services like Power Virtual Agents can consume LU files to train their internal custom NLU recognizer. The open source community have created LU parsers which target open source NLU engines from Spacey.io and BERT models.

Ultimately, the bot application needs to be able to decide which component is supposed to handle an input. Each component can use different recognizers to process input, so we need a standardized way of creating a dispatch model, which is a model that routes traffic to the correct component. This lays the foundation for organizing a bot by department or integrating “skills” from other sources.

To create dispatch models, we have defined how each component can expose language data (again leveraging the technology agnostic .LU declarative file format) that exposes the information necessary for the caller to create a dispatch model.

Our current dispatch tooling utilizes LUIS for dispatch models, but have a new preview version which has been renamed Orchestrator. The orchestrator uses transformer-based language models (e.g.: BERT, RoBERTa) to build the dispatch model. This model has been optimized to be small enough to be embeddable (~200mb for English), work for low latency scenarios, and use the information from the .LU file to build a rich dispatch model that can be used offline, in embedded scenarios, and offered as part of Azure Bot Service.

Capstone seems to provide pkg-config .pc file. So, the following should work:

By minimizing the number of layers you're reducing the size of your image and yes, also build time. This is also recommended in best practices section "Minimize number of layers".

In older versions of Docker, it was important that you minimized the number of layers in your images to ensure they were performant. The following features were added to reduce this limitation.

• Only the instructions RUN, COPY, ADD create layers. Other instructions create temporary intermediate images, and do not increase the size of the build.

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Figured it out, and took many hours of scouring the internet..

All that is needed is this:

For anyone who comes across this issue later, I found out that pants stores an html file of all the code coverage which was easy enough to parse.

Missing return between #endif and }.

But there is return word in the bottom of the function.

No there isn't a return.

The following is one big comment with #if 0

The solution is actually part of your question. If we have a second look at the composer error:

Problem 1 - This package requires php >=7.1.3 but your PHP version (7.0.27; Package overridden via config.platform [...]

This means you do have something like the following in your composer.json, which you need to change or remove prior to the update:

try chmod +x /var/apps/importsup/importparcoursup.jar to make the file executable.